Independently Recurrent Neural Networks
Simple TensorFlow implementation of Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN by Shuai Li et al. The author's original implementation in Theano and Lasagne can be found in Sunnydreamrain/IndRNN_Theano_Lasagne.
In IndRNNs, neurons in recurrent layers are independent from each other. The basic RNN calculates the hidden state
h = act(W * input + U * state + b). IndRNNs use an element-wise vector multiplication
u * state meaning each neuron has a single recurrent weight connected to its last hidden state.
- can be used efficiently with ReLU activation functions making it easier to stack multiple recurrent layers without saturating gradients
- allows for better interpretability, as neurons in the same layer are independent from each other
- prevents vanishing and exploding gradients by regulating each neuron's recurrent weight
Copy ind_rnn_cell.py into your project.
from ind_rnn_cell import IndRNNCell # Regulate each neuron's recurrent weight as recommended in the paper recurrent_max = pow(2, 1 / TIME_STEPS) cell = MultiRNNCell([IndRNNCell(128, recurrent_max_abs=recurrent_max), IndRNNCell(128, recurrent_max_abs=recurrent_max)]) output, state = tf.nn.dynamic_rnn(cell, input_data, dtype=tf.float32) ...
Experiments in the paper
See examples/addition_rnn.py for a script reproducing the "Adding Problem" from the paper. Below are the results reproduced with the
See examples/sequential_mnist.py for a script reproducing the Sequential MNIST experiment. I let it run for two days and stopped it after 60,000 training steps with a
- Training error rate of 0.7%
- Validation error rate of 1.1%
- Test error rate of 1.1%
- Python 3.4+
- TensorFlow 1.5+